
'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor

class       UpperCAmelCase					(       unittest.TestCase			):





		"""simple docstring"""





		def __init__(     self	,       _snake_case	,       _snake_case=7	,       _snake_case=3	,       _snake_case=18	,       _snake_case=30	,       _snake_case=400	,       _snake_case=True	,       _snake_case=32	,       _snake_case=True	,       )					->     List[Any]:
				_UpperCamelCase							:			str     =   parent
				_UpperCamelCase							:			List[Any]     =   batch_size
				_UpperCamelCase							:			List[str]     =   num_channels
				_UpperCamelCase							:			Tuple     =   image_size
				_UpperCamelCase							:			Tuple     =   min_resolution
				_UpperCamelCase							:			Any     =   max_resolution
				_UpperCamelCase							:			Any     =   do_resize
				_UpperCamelCase							:			Dict     =   size_divisor
				_UpperCamelCase							:			List[Any]     =   do_rescale




		def _lowercase	(     self  )					->     Union[str, Any]:
				return {
				    "do_resize": self.do_resize,
				    "size_divisor": self.size_divisor,
				    "do_rescale": self.do_rescale,
				}







@require_torch
@require_vision
class       UpperCAmelCase					(       lowerCamelCase__	,		unittest.TestCase			):





		"""simple docstring"""





		A__	: List[str]    =     GLPNImageProcessor if is_vision_available() else None
		def _lowercase	(     self  )					->     str:
				_UpperCamelCase							:			Any     =   GLPNImageProcessingTester(self  )
		@property
		def _lowercase	(     self  )					->     Tuple:
				return self.image_processor_tester.prepare_image_processor_dict()
		def _lowercase	(     self  )					->     List[str]:
				_UpperCamelCase							:			int     =   self.image_processing_class(**self.image_processor_dict  )
				self.assertTrue(hasattr(__snake_case	,       '''do_resize'''  )  )
				self.assertTrue(hasattr(__snake_case	,       '''size_divisor'''  )  )
				self.assertTrue(hasattr(__snake_case	,       '''resample'''  )  )
				self.assertTrue(hasattr(__snake_case	,       '''do_rescale'''  )  )
		def _lowercase	(     self  )					->     Tuple:
				pass
		def _lowercase	(     self  )					->     str:
				# Initialize image_processing
				_UpperCamelCase							:			Tuple     =   self.image_processing_class(**self.image_processor_dict  )
				# create random PIL images
				_UpperCamelCase							:			Union[str, Any]     =   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__snake_case  )
				for image in image_inputs:
						self.assertIsInstance(__snake_case	,       Image.Image  )

				# Test not batched input (GLPNImageProcessor doesn't support batching)
				_UpperCamelCase							:			int     =   image_processing(image_inputs[0]	,       return_tensors='''pt'''  ).pixel_values
				self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
				self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )
		def _lowercase	(     self  )					->     List[Any]:
				# Initialize image_processing
				_UpperCamelCase							:			Optional[Any]     =   self.image_processing_class(**self.image_processor_dict  )
				# create random numpy tensors
				_UpperCamelCase							:			List[str]     =   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__snake_case	,       numpify=__snake_case  )
				for image in image_inputs:
						self.assertIsInstance(__snake_case	,       np.ndarray  )

				# Test not batched input (GLPNImageProcessor doesn't support batching)
				_UpperCamelCase							:			Union[str, Any]     =   image_processing(image_inputs[0]	,       return_tensors='''pt'''  ).pixel_values
				self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
				self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )




		def _lowercase	(     self  )					->     Union[str, Any]:
				# Initialize image_processing
				_UpperCamelCase							:			Optional[Any]     =   self.image_processing_class(**self.image_processor_dict  )
				# create random PyTorch tensors
				_UpperCamelCase							:			Tuple     =   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__snake_case	,       torchify=__snake_case  )
				for image in image_inputs:
						self.assertIsInstance(__snake_case	,       torch.Tensor  )

				# Test not batched input (GLPNImageProcessor doesn't support batching)
				_UpperCamelCase							:			Optional[Any]     =   image_processing(image_inputs[0]	,       return_tensors='''pt'''  ).pixel_values
				self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
				self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)